Artificial Intelligence (AI) is intelligence demonstrated by machines as opposed to human or animal intelligence. AI is empowering change and innovation with the aid of hardware and software, as well as a multitude of related tools.
AI already plays a role in our daily lives, from surfing the internet or using smartphones to navigating your way around the neighborhood using a global positioning system (GPS) of sorts, streaming your favorite music, acknowledging the predictive text that comes up on your smartphone, the grammar check when composing an email or message, image editing on your photographs, playing with your latest drone, shopping online and that feeling of safety when arming your security devices at home, not to mention the numerous ads that appear on the edge of your screen after having browsed or bought items online. Have you ever wondered how Gmail gets its very accurate, “smart reply” options based on the content of an email you have just received?
Tasks that require human intelligence can now be done by AI, increasing efficiency in organizations and enabling personnel to tackle other problems. In the field of medicine, a quick diagnosis based on stored information about the patient’s medical history and an evaluation of current symptoms can save a life.
If you are something of a math geek, and statistical decision-making sounds like a lot of fun, then you are sure to find the subject of statistical pattern recognition as fascinating as we do. With your strong bent for linear algebra and statistics, you may want to consider delving into this innovative field of technology in the long term with an online master’s in computer science for a career that offers job satisfaction and ongoing personal development.
Get involved in the fascinating field of statistical pattern recognition, which involves identifying problems, finding patterns and facilitating innovative solutions in fields, such as ecology, social sciences, medicine, and genetics.
AI: big data, machine learning, data science, and pattern recognition
So, what makes AI tick? Big data, machine learning, data science and data recognition are all subsets of AI but are also separate areas of expertise.
Big data: Often the fuel of AI (but not always), big data is the stored information on everything from your latest social media post to who you bank with, your country’s GDP and world food shortages. Without all this data, AI would not function.
Machine learning: ML is the utilization of data to understand scenarios and to “learn” from the data in order to make predictions and take decisions without being specifically programmed to do so. Machine learning is used in cases where it is not feasible or too complicated to build algorithms for the task at hand. Examples of this are medical diagnosis and speech recognition.
Pattern recognition: This plays an important role in AI. Without pattern recognition, fingerprint scanning and facial recognition, for example, would not exist, as there would be no way of telling a machine how to do these things. Computers manage pattern recognition by scanning points and, based on algorithmic instructions, referencing the points to a pattern that has been stored in the data.
What is pattern recognition? People are able to recognize the differences between, say, the sound of a cat meowing and that of a dog barking. We can tell the difference between the various colors or between cows and sheep.
Of course, we need to have a frame of reference before we can recognize any of the above. A young child who has never seen a farmyard full of animals may not know the difference between cows and sheep.
- Pattern recognition enables the automation of processes. such as fingerprint identification and speech recognition, medical diagnosis, optical character recognition, DNA sequence identification and more.
- Pattern recognition impacts our daily lives in so many ways to a point that we are often unaware of the connection.
- Mobile phones and emails predict words to follow, based on recognized patterns of speech.
- In the medical field, MRI and CT scans use pattern recognition to recognize and diagnose disease or injury.
How do we teach a computer to recognize different sounds or objects? Computers use pattern recognition, based on what we tell them to search for and recognize their target object. The problem is that these patterns are very complex and contain a large amount of information.
To facilitate pattern recognition, we make inferences from perceptual data using tools, such as statistics, computational geometry, machine learning, probability, signal processing and algorithms.
There are two approaches to pattern recognition: statistical and structural (syntactic) recognition. To put it into perspective, structural pattern recognition is the use of data of a morphological, or structured nature, whereby the interrelationships can be identified in the different groups of data. Structural pattern recognition can be used when there is a clear structure of patterns.
Identification of structural data is often complex, and deep knowledge of the subject is required in order to identify all the data points necessary for recognition. As an example, the creation of a model to identify and correct grammatical errors in writing would require an in-depth knowledge of the language, including spelling, words, grammatical rules and the use of phrases and would be particularly difficult to predict and correct in the case of a non-native learner’s writing.
Statistical pattern recognition
Statistical pattern recognition, on the other hand, draws on established concepts and statistical techniques for the analysis of data in order to extract information and make reliable decisions.
Statistical pattern recognition uses a wide range of techniques, such as the Bayes’ theorem used in Bayesian inference, neural networks, support vector machines, feature selection techniques and feature reduction techniques.
Typically, raw data is processed and converted into a format that is usable for the particular exercise. Pattern recognition techniques are then applied to classify the data and organize it into clusters. The classification process assigns labels to the different classes of data. This is known as “supervised learning”, as it uses a set of training patterns or domain knowledge. Unsupervised learning is the clustered data that assists with specific decision-making activities.
The importance of statistics in AI
In an article published by Springer Link, “Is there a role for statistics in artificial intelligence?” Friedrich, Antes, et al. demonstrate the importance of statistics in AI with an example of the entire process of the establishment of an AI application, beginning with the empirical analysis of the research question: the design, analysis and interpretation.
Design: This is a four-step process: validation, representativity, selection of variables and bias reduction.
Validation: Different traditions exist for AI and statistics. ML has a longstanding benchmark tradition that often uses many datasets for evaluation.
Statistics on the other hand:
- Rely on theory and simulations, augmented by a couple of strong data samples.
- Mathematical proofs, theoretical investigations and detailed simulation studies are carried out to evaluate the method’s limits.
- Statistics makes use of probabilistic models in order to reflect real-life diversity.
Sample size: If the AI input is high-dimensional, i.e., a large number of variables with a diverse range of possible values, but with small samples, this is difficult to handle and typically requires a large amount of training data.
Statisticians are able to:
- Assess the potential and limits of an AI exercise by using statistical models and corresponding mathematical approximations or numerical simulations.
- Estimate the number of cases needed in the planning stage of the project. This requires advanced statistical training and expertise.
Representativity: The idea that large data sets are sufficient for an exercise is incorrect. History has demonstrated some erroneous and occasionally devastating results based on this assumption.
Bias reduction: If data collection is not circumspect, spurious correlations (not causally related) and bias can falsify the conclusions. While bias in statistics generally refers to the deviation between the estimated and true value of the parameter, there are other types of bias, such as cognitive bias, the length of time that data is collected and the disregard of sub-groups.
Statistics provide methods for minimizing bias. For example, the assessment of risk in medicine, classification into groups, marginal analysis, consideration of interactions, a meta-analysis of previous studies and data collection techniques, such as randomization, blinding and methods of “optimal designs” (experimental designs that are optimal with respect to some statistical criterion).
Assessment of data quality
In AI, relevance, completeness, availability, timeliness, meta-information and documentation are the criteria, with data often being extracted, transformed and loaded without due consideration of their high-density nature.
In contrast, the statistical principles of relevance, accuracy and reliability, timeliness and punctuality, coherence and comparability, and accessibility and clarity are likely to produce a more reliable data set.
The distinction between causality and association
The rapid development of AI in recent years has led to a vast number of tools in the form of algorithms and models. The high predictive power of AI enables the uncovering of structures and relationships in large volumes of data based on association. AI methods are frequently used in medicine to analyze observational data that has not been collected within the strict framework of a randomized study design.
However, the discovery of associations and correlations is not equivalent to establishing causal claims. The Springer Link article suggests that an important consideration for AI is to replace associational argumentation with causal argumentation.
In recent years, proposals for uncertainty quantification have already been developed in AI, making use of Bayesian approximations, jackknifing, bootstrapping and other cross-validation methods, but there is still some work to be done in this area.
A suggestion was to build algorithms into statistical models for better quantification of the underlying uncertainty and better interpretation of the results. Statistics can help to increase the interpretability and validity of AI by contributing to the quantification of uncertainty. The use of probabilistic models or dependency structures would allow for the inclusion of comprehensive mathematical investigations.
Differentiation between causality and associations: the answering of causal questions; consideration of covariate (influencing outcome but not of direct interest) effects; simulation of interventions.
Assessment of certainty or uncertainty in results: increasing interpretability; provision of scholastic simulation designs; mathematical validity proofs or theoretical properties in AI contexts; accurate analysis of the quality of algorithms.
In conclusion, Friederich and colleagues state: “Statistical methods must be considered an integral part of AI systems, from the formulation of the research questions, the development of the research design, from the analysis through to the interpretation of the results.”
Examples of contributions to AI by statisticians include the following:
Methodological development: improved learning algorithms based on robust and penalized estimation methods.
Planning and design: Statistics assists with the optimization of data collection and the preparation thereof for further evaluation. The quality measures and their associated inference methods also help in the evaluation of AI models.
Assessment of data quality and data collection: In addition to the wide range of data-analysis tools, comprehensive parameter tuning is possible with the help of model-based statistical methods.
Differentiation of causality and associations: Statistical methods of dealing with covariate effects can be used, bearing in mind the different relationships covariates can have between treatment and outcome and bias in the estimation of causal effects. The integration of causal methods into AI can contribute to the transparency and acceptance of AI methods.
Assessment of certainty or uncertainty in results: With the use of statistical models, mathematical proofs of validity are possible, and limitations of the methods can be explored through simulation designs.
AI and the future
AI is shaping our future in so many ways. It’s exciting and fascinating. It makes life easier on a lot of levels and is undoubtedly going to continue improving by leaps and bounds. Whether you are considering a formal qualification in computer science or not, it’s a good idea to keep abreast of the changes. There are numerous websites out there with the latest AI news and information, keeping you up to date with the latest developments and helping you to make informed decisions about the technology that affects your life.